Bibliographic record
Abstract
The term nowcast is used to emphasize that a forecast is being produced for very short lead times, typically 0 to 6 h. Most nowcasting systems use weather radar as the primary tool for forecasting. Modern techniques use computer algorithms to compute these short-term forecasts, and have been shown to have more skill than human forecasters or numerical models alone. Applications of nowcasting span across many industries, with aviation being of particular importance. The essence of nowcasting can be summarized in two steps: (1) obtain a motion estimate for an existing storm, and (2) advect the current storm using the derived motion estimate. Since the inception of weather radar, nowcasting methods have noticeably improved, particularly with respect to estimating storm motion. However, nowcasting is still largely based on Lagrangian persistence, where the forecast field (i.e. reflectivity) is held constant in the Lagrangian frame. This has been done principally because of the difficulties associated with forecasting storm growth and decay. In this paper, an attempt is made to incorporate storm growth and decay into the McGill Algorithm for Precipitation Nowcasting by Lagrangian Extrapolation (MAPLE), by using mesoscale parameters to physically constrain the evolution storm systems. The analysis is conducted on a continental scale, over the contiguous United States. The parameters selected for study were equivalent potential temperature (thetae) and convective available potential energy (CAPE), because they have been shown by theory and observations to be directly related to the intensity and duration of storm systems (Zawadzki and Ro 1978 and Zawadzki et al. 1981, 1994). While past studies were based on observations on a local scale, valid on a day-to-day basis, the current work is applied to a continental scale, on an hour-by-hour basis. Results of the study show little promise for application to nowcasting. No correlations were found between various measures of storm growth/decay and the mesoscale parameters. Several explanations for the results are proposed, which include poor data quality, insufficient sample size and the possibility of a land-surface feedback.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".